Systems and methods regarding point-of-recognition optimization of onsite user purchases at a physical location

ABSTRACT

Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/047,305, filed on Jul. 27, 2018, which is a continuation of U.S.patent application Ser. No. 15/953,187, filed on Apr. 13, 2018, nowissued as U.S. Pat. No. 10,062,069, which is a continuation-in-part ofU.S. patent application Ser. No. 15/637,675, filed on Jun. 29, 2017, nowissued as U.S. Pat. No. 9,978,086. The entirety of each of the foregoingapplications are incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to point-of-recognitionoptimizer systems and methods, and, more particularly, topoint-of-recognition optimizer systems and methods for optimizing onsiteuser purchases at physical locations.

BACKGROUND

Firms or entities that own or operate physical stores, which allow foronsite consumer shopping, such as Wal-Mart, have encountered significantcompetition from online, virtual stores such as Amazon. Physical storeowners or operators have responded to the threat of virtual stores withtheir own versions of online, virtual store websites. However, suchdirect competition has so far achieved a limited amount of successrelative to popular online stores such as Amazon.

New competition from virtual stores has not only impacted the marketshare and sales of physical store owners or operators, but has alsodisrupted advertising firms that deliver advertisements to consumers viaprint, electronic, or other media outlets. In addition, conventionaladvertising firms do not have direct access to consumer data that can beused to indicate specific consumers' needs and preferences and thatwould otherwise allow the advertising firms to better target the needsand preferences of the specific consumers.

In addition, today's physical stores rely on time-intensive manualcheckout procedures that require store clerks to individually handleproducts, which can include identifying a product's Universal ProductCode (UPC) barcode, scanning that UPC into a traditional point-of-salesystem, and otherwise finalizing a purchase transaction for theconsumer. While new technologies exist that can eliminate the need for aUPC scanner, such newer technologies still require manual checkout thatrely on time-consuming store clerks and checkout lines that can detractfrom the overall onsite consumer shopping experience.

SUMMARY

In order to overcome the aforementioned issues, embodiments forpoint-of-recognition optimizer systems and methods are described hereinfor optimizing onsite user purchases at physical locations. Firms orentities that own or operate physical stores can use thepoint-of-recognition optimizer systems and methods to improve consumerin-store shopping experiences and, therefore, better compete againstonline, virtual stores like Amazon. The point-of-recognition optimizersystems and methods also provide the opportunity for the firms orentities to compete for advertising revenue against data-intensive firmssuch as Google and Facebook. As described herein, thepoint-of-recognition optimizer systems and methods allow for physicalstores to optimize consumer purchases, checkout, and the collection anduse of user-centric information.

In various embodiments, a point-of-recognition optimizer system caninclude one or more processors and one or more computer memories, wherethe system can be configured to receive, via a first computertransmission, a purchasable-unit identifier (ID) associated with apurchasable-unit. A purchasable-unit, may be, for example, a unit of aretail product located onsite at a physical store, where thepurchasable-unit ID may uniquely identify the unit and/or retailproduct. In some embodiments, the purchasable-unit may have a pluralityof surfaces and the purchasable-unit ID may be identifiable to theoptimizer device on each of the plurality surfaces as described herein.

In some embodiments, the purchasable-unit may be a product manufacturedand/or distributed by a particular manufacturer to the physical storeand made available to consumers onsite. In other embodiments, thepurchasable-unit may be a product distributed on behalf of amanufacturer by a product wholesaler to the physical store, and,therefore available to consumers onsite.

In various embodiments, a purchasable-unit and its relatedpurchasable-unit ID may be identified or recognized by an optimizerdevice associated with a consumer or user. For example, the recognizedpurchasable-unit can be located onsite with the optimizer device, sothat a consumer or user can use the optimizer device to identify therecognized purchasable-unit and its related purchasable-unit ID. Theoptimizer device can include one or more processors for executing thesystems and methods disclosed herein. For example, some embodiments theoptimizer device may be a mobile phone or other portable electronicdevice, such as a tablet, of the user. In other embodiments, theoptimizer device may include two separate devices including a hand-heldproduct identifier device for identifying purchasable units and adisplay screen device for displaying offers as further described herein.

In some embodiments, the purchasable-unit ID may be used to determine aplurality of competing purchasable-units. For example, the plurality ofcompeting purchasable-units can include the recognized purchasable-unitidentified by the optimizer device and one or more additionalpurchasable-units offered by competing manufacturers, wholesalers, orother distributors.

In various embodiments, the point-of-recognition optimizer system cantransmit, via a second computer transmission, an offer for an offeredpurchasable-unit to the optimizer device, where the user can view theoffer on the optimizer device. The offer may originate from a particularpurchasable-unit distributor, such as a manufacturer or wholesaler ofthe purchasable-unit.

In certain embodiments, the particular distributor that sent the offermay have outbid other distributors who competed for the opportunity forthe optimizer device, and therefore the user, to receive the offer. Forexample, the outbidding purchasable-unit distributor may have beenchosen from a plurality of competing purchasable-unit distributors,where each of the plurality of competing purchasable-unit distributors,including the outbidding purchasable-unit distributor, may correspond toa respective plurality of competing purchasable-units. The plurality ofcompeting purchasable-units may include the recognized purchasable-unit,the offered purchasable unit, and any remaining purchase-units havesimilar or competing features. For example, in one embodiment, theplurality of competing purchasable-units can include a same type ofpurchasable-unit, where the purchasable-units are commodities ornear-commodities to one another. In other embodiment, for example, theplurality of competing purchasable-units can include two or morepurchasable-units of different types, where the products are notcommodities, but instead offer competing features or that can be used ina manner such that a consumer could choose one product over the other.

In various embodiments, each of the competing purchasable-unitdistributions, which includes the outbidding purchasable-unitdistributor, can access a user-centric information profile in order togenerate competing bids. As described herein, the user-centricinformation profile may include details about the user's past purchasehistory or other personal information to allow the distributors togenerate informed and data-driven offers or advertisements targeted tothe user. The outbidding purchasable-unit distributor is the distributorthat wins the bidding process and therefore is able to generate theoffer received by the optimizer device, and, therefore the user.

For example, in one embodiment, the outbidding purchase-unit distributormay be the distributor that agrees to pay a highest distributor fee,with respect to each of the other distributors, to the owner or operatorof the store for the opportunity of the user's optimizer device toreceive the offer as described herein.

In other embodiments, the outbidding purchase-unit distributor may alsobe the distributor that submits or bids a best value to the user orconsumer, such that the offer includes an offer value that is mostfavorable to the consumer. In such embodiments, the offer value may bebased on the user's user-centric information.

In some embodiments, the optimizer device may be operable to receive theuser's information in order to generate or update the user's user-centerinformation profile. For example, the optimizer device may include adisplay unit and an input unit. The display unit, such as a displayscreen of a mobile device, may be operable to display the offer and theuser-centric information to the user. The input unit, such as a keyboardor keypad of a mobile device, may be operable to receive user-centricinformation for generating or further updating the user-centricinformation profile. The input unit may also be operable to receivelogin information for activating the optimizer device in order toprotect the user's user-centric information or preclude other,non-authorized, users from making purchases on the user's behalf.

In other embodiments, the optimizer device may include an optical unit,such as a camera, sensor, or scanning device, that the user can use totake digital images or source other such optical information fromdocuments including, for example, the user's automobile registrations,tax bills, utility bills, real estate records, or other such records,and use that optical information to generate or update the user-centricinformation profile. For example, the user may take a picture of his orher automobile registrations, utility bills, etc., where suchinformation may be uploaded to the point-of-recognition optimizer systemand used to generate or update the user-centric information profile.

In various embodiments, the plurality of competing purchasable-unitdistributors can include one or more product manufacturers and one ormore product wholesalers who distributed the corresponding competingpurchasable-units to a physical store for onsite identification andselection by consumers.

In some embodiments, the offered purchasable-unit may be the same as therecognized purchasable-unit. For example, in such embodiments, the offermay originate from the distributor of the recognized purchasable-unit,where the distributor of the recognized purchasable-unit outbid allother distributors for the opportunity of the optimizer device toreceive the offer.

In other embodiments, the offered purchasable-unit is different from therecognized purchasable-unit. For example, in such embodiments, the offermay originate from a new distributor, different from the distributor ofthe recognized purchasable-unit, where the new distributor outbid allother distributors, including the distributor of the recognizedpurchasable-unit, for the opportunity of the optimizer device to receivethe offer.

In various embodiments, the optimizer device may facilitate a userpurchase. For example, in some embodiments the optimizer device may beconfigured to detect that a chosen purchasable-unit has been associatedwith the user. The chosen purchasable-unit can be a purchasable-unitthat the user choses to purchase, which can be any of the recognizedpurchasable-unit, the offered purchasable-unit, or any of the otherplurality of competing purchasable-units onsite at the physicallocation. In any event, the chosen purchasable unit can be identifiedwith a chosen purchasable-unit identifier (ID) that uniquely identifiesthe chosen purchasable-unit identifier (ID).

In some embodiments, the chosen purchasable-unit may be detected withone or more sensors associated with a user container when the userplaces the chosen purchasable-unit in the user container. In someembodiments, the user container can be, for example, a shopping cart,shopping basket, or other designated location or container at thephysical location that the user places the chosen purchasable-unit.

In several embodiments, the optimizer device can initiate a purchaserequest, based on the chosen purchasable-unit identifier (ID), topurchase the chosen purchasable-unit when the user is within a proximityto an exit of the physical location.

Based on the purchase request, in several embodiments, the optimizerdevice can cause an update to a user-centric information profileassociated with the user.

In some embodiments, the optimizer device may be further configured todetermine that an interference threshold value has been passed regardingthe user's interaction with the user container, and generate, based onthe interference threshold value, an alert, the alert indicating toonsite personnel to assist the user.

In still further embodiments, the offered purchasable-unit may not beavailable onsite at the physical location. In such embodiments, the usermay purchase the offered purchasable-unit, via the optimizer device, fordelivery of the offered purchasable-unit to an address specified by theuser.

Accordingly, the benefits of the point-of-recognition optimizer systemsand methods are designed to incentivize both the consumer and the firmor entity that owns or operate the physical, onsite store or location,to enhance the consumer onsite shopping experience, and, thereforepromote onsite, physical store sales. For example, consumers can benefitfrom competition-induced lower prices encouraged by the distributorscompeting against one another to provide consumers with various offersthat can include discounts, advertisements for new or differentproducts, or other such incentive-based offers. For example, asdescribed herein, the point-of-recognition optimizer systems and methodsallow for distributors to access a user's user-centric informationprofile to analyze information about the consumer and bid on theopportunity to transmit offers, which can include advertisements andcoupons, at the exact time when consumers are about to make a purchasedecision.

Moreover, the point-of-recognition optimizer system and methodsmotivates consumers to participate in onsite, physical store sales,because when consumers provide verifiable consumer information (e.g.,tax bills, auto insurance cards showing make, model, and year of ownedautomobiles, etc.), consumers can receive targeted, relative, andpotentially more valuable offers from the competing purchasable-unitdistributors. In addition, the competing purchasable-unit distributorscan reliably verify each consumer's purchasing power based on theconsumer's user-centric information profile.

In addition, the firm or entity that owns or operates a store may alsobenefit from the increased consumer traffic, and potential revenue, thatarises from consumer motivation for receiving the competing offers frommultiple competing purchasable-unit distributors as described herein.Moreover, from the point of view of the firm or entity, thepoint-of-recognition optimizer systems and methods can be far lesscapital intensive, and can scale in proportion to consumer use, whencompared with other competing point-of-sale systems or checkoutprocedures that can require additional checkout lanes, etc. This isespecially true when compared with point-of-recognition optimizerembodiments where consumers bring and use their own optimizer devices,such as their mobile phones, as described herein. Accordingly, by usingthe point-of-recognition optimizer systems and methods, consumers canavoid the conventional time-consuming checkout process while onsite,physical store productivity significantly improves.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred embodiments whichhave been shown and described by way of illustration. As will berealized, the present embodiments may be capable of other and differentembodiments, and their details are capable of modification in variousrespects. Accordingly, the drawings and description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an exemplary computer network in accordance withvarious embodiments of the point-of-recognition optimizer systems andmethods described herein.

FIG. 2 illustrates an embodiment of a consumer-oriented purchasetransaction using the point-of-recognition optimizer systems and methodsdescribed herein.

FIG. 3 illustrates an embodiment of a consumer purchase procedure inaccordance with the point-of-recognition optimizer systems and methodsdescribed herein.

FIG. 4 illustrates an embodiment of a checkout process in accordancewith the point-of-recognition optimizer systems and methods describedherein.

FIG. 5 illustrates an embodiment of method for optimizing user purchasesat a physical location in accordance with the disclosures herein.

The Figures depict preferred embodiments for purposes of illustrationonly. Alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary computer network 100 in accordance withvarious embodiments of the point-of-recognition optimizer systems andmethods described herein. The computer network 100 includes one or morelocal point-of-recognition optimizer servers 104. The localpoint-of-recognition optimizer servers 104 may include one or moreprocessors configured to optimize onsite user purchases at a physicallocation as described herein. The local point-of-recognition optimizerservers 104 may implement any number of web-based platforms such asMicrosoft ASP.NET, Java Server Pages (JSP), Ruby on Rails, or other suchweb-based platforms in order to receive and respond to computertransmissions as described herein. The local point-of-recognitionoptimizer servers 104 may further include one or more computer memoriesfor storing information, such as user-center information profiles andother user information as described herein. The localpoint-of-recognition optimizer servers 104 may also implement one ormore database platforms for storing and organizing the user-centerinformation profiles and other user information, which may include, forexample, Oracle Database, IBM DB2, MySQL, MongoDB, or other suchdatabase platforms.

The local point-of-recognition optimizer servers 104 may be locatedonsite at a physical location, such as store 102. Store 102 may be aphysical retail location offering purchasable-units to consumers, suchas user 106, as described herein. For example, store 102 may be anelectronic retail store that sells numerous competing retail electronicproducts. In other examples, store 102 may be a general retail storethat sells a variety of different types or kinds of purchasable-unitproducts. In still further embodiments, store 102 may be amembership-based store that sells wholesale products direct toconsumers.

In some embodiments, the point-of-recognition optimizer system mayoperate remotely. For example, as shown in FIG. 1, remotepoint-of-recognition optimizer servers 140, which are similar to localpoint-of-recognition optimizer servers 104, communicate with store 102remotely through a network 130. Network 130 allows computertransmissions to be received and transmitted to and from the remotepoint-of-recognition optimizer servers 140. In some embodiments, network130 may be a private network that connects store 102, and its localpoint-of-recognition servers 104, to the remote point-of-recognitionoptimizer servers 140. In other embodiments, network 130 may be a publicnetwork, such as the Internet, where store 102, and its localpoint-of-recognition servers 104, and remote point-of-recognitionoptimizer servers 140 communicate over conventional Internet protocolsand standards, for example, including the Hyper Text Transfer Protocol(HTTP), Transfer Control Protocol (TCP), and the Internet Protocol (IP).The remote point-of-recognition optimizer servers 140 may be similarlyconfigured to the local point-of-recognition optimizer servers 104 suchthat the remote point-of-recognition optimizer servers 140 also includeone or more processors configured to optimize onsite user purchases at aphysical location as described herein. Similarly, the remotepoint-of-recognition optimizer servers 140 may also include one or morememories for storing information, such as user-center informationprofiles and other user information as described herein. The remotepoint-of-recognition optimizer servers 140 may also implement one ormore web-based platforms and/or database platforms as described for thelocal point-of-recognition optimizer servers 104. The remotepoint-of-recognition optimizer servers 140 may also include a localterminal 142, where an operator of the remote point-of-recognitionoptimizer servers 140 may view, update, or modify information, such asuser information or user-centric information profiles as describedherein. Local terminal 142 may also be used to manage and maintain thepoint-of-recognition optimizer system, including diagnose or maintainany of the remote point-of-recognition optimizer servers 140 or thelocal point-of-recognition optimizer servers 104, where the localpoint-of-recognition optimizer servers 104 are accessed via network 130.

In some embodiments, the point-of-recognition optimizer systems andmethods may be implemented only on local point-of-recognition optimizerservers 104. In other embodiments, the point-of-recognition optimizersystems and methods may be implemented only on the remotepoint-of-recognition optimizer servers 140. In still further embodimentsthe point-of-recognition optimizer systems and methods are implementedvia a hybrid approach where both the local point-of-recognitionoptimizer servers 104 and the remote point-of-recognition optimizerservers 140 implement the point-of-recognition optimizer systems andmethods. For example, in one hybrid embodiment, the localpoint-of-recognition optimizer servers 104 may implement thefunctionality regarding receiving a purchasable-unit identifier (ID)associated with a recognized purchasable-unit identified by an optimizerdevice, determining a plurality of competing purchasable-units based onthe purchasable-unit ID, and transmitting an offer for an offeredpurchasable-unit to the optimizer device, where the remotepoint-of-recognition optimizer servers 140 may implement thefunctionality of receiving a purchase request and updating auser-centric information profile associated with the user based on thepurchase request. As contemplated herein, various other hybridembodiments may also be implemented, where at least a portion of theoptimizer functionality is implemented via the localpoint-of-recognition optimizer servers 104 and the remaining or otherportions are implemented via the remote point-of-recognition optimizerservers 140.

User 106 may be a consumer onsite at store 102. User 106 may interactwith the point-of-recognition optimizer systems, such as provided by anyof the local point-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140, via an optimizer device 110.Optimizer device 110 may be a portable electronic device that mayinclude one or more processors, one or more computer memories, a displayunit (e.g., a display screen) for displaying information to user 106,and an input unit (e.g., a keyboard) for receiving information from user106. For example, the optimizer device 110 may be any of a tablet device112, mobile phone 114, or other such portable electronic device 116.

In one embodiment, the optimizer device 110 may be provided by the owneror operator of store 102 to user 106 when user 106 enters store 102. Forexample, user 106 may receive the optimizer device from store personnel(or from a designated pickup location) of store 102 when user 106 entersthe store, where the user 106 may return the optimizer device 110 to thestore personnel (or designed pickup location) when user 106 exits thestore. The optimizer device 110 may be configured to interact with anyof the local point-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140 to optimize onsite userpurchases at store 102 as described herein.

In another embodiment, user 106 may own or otherwise control theoptimizer device 110 outside of store 102. For example, the optimizerdevice 110 may be the user 106's own mobile phone, such as mobile phone114. In such embodiments, user 106 can download an optimizer mobileapplication (e.g., an “Optimizer App”) onto mobile phone 114 in order toallow mobile phone 114 to communicate with any of the localpoint-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140. The Optimizer App may beimplemented via a mobile application platform including, for example,Apple iOS, Google Android, or any other such mobile applicationplatform, to optimize onsite user purchases at store 102 as describedherein.

The optimizer device 110 may also include a transceiver for transmittingand receiving computer transmissions to and from network 130 and,therefore, through network 130 to any of the local point-of-recognitionoptimizer servers 104 or the remote point-of-recognition optimizerservers 140. As illustrated by FIG. 1, network 130 may include awireless transceiver 126 for facilitating transmissions 124 to and fromuser 106's optimizer device 110. In some embodiments, the transceiver126 may also be located onsite at store 102 and may receive and transmittransmissions 124 through a private store network 123. In oneembodiment, transceiver 126 may be a WiFi access point implementing, forexample, the IEEE 802.11 standard for electronic wireless networkaccess, where wireless transmissions 124 are received and transmitted toand from transceiver 126 and routed through private store network 123for interaction with the point-of-recognition optimizer system, whichcan include interaction with any of the local point-of-recognitionoptimizer servers 104 or the remote point-of-recognition optimizerservers 140. In present embodiment, the transceiver 126 may be locatedonsite at store 102 such that when user 106 enters store 102, thepoint-of-recognition optimizer system can begin to interact with theuser 106's optimizer device 110 through private store network 123 andtransceiver 126, for example, by requesting login information from theuser 106 in order to activate the optimizer device 110 with thepoint-of-recognition optimizer system.

In another embodiment, the transceiver 126 may be a cellular networktower, base station, or other mobile phone base station that can sendand receive transmissions 124 to and from optimizer device 110. Thetransmissions 124 may be based on any of a number of mobilecommunication standards including GSM, EDGE, UMTS/UTRA, 3GPP, LTE, CDMA,UMB, or other such mobile phone standards. The transmissions 124 may besent to and from the optimizer device 110 via a mobile base stationtransceiver 126, where the transmissions may be routed through network130 to any of the local point-of-recognition optimizer servers 104 orthe remote point-of-recognition optimizer servers 140. In the presentembodiment, the optimizer device 110 may include a global positingsatellite (GPS) unit, such as a GPS microchip within the optimizerdevice 110, which may be used to determine the position of user 106. Forexample, the GPS unit can detect when user 106 enters store 102, so thatthe point-of-recognition optimizer system can begin to interact with theuser 106's optimizer device 110, for example, by requesting logininformation from the user 106 in order to active the optimizer device110 with the point-of-recognition optimizer system.

User 106 may also interact with a user container 108, which, in someembodiments, can be an optimizer shopping cart or shopping basket asdescribed herein. The user container 108 may include one or moresensors, such as infrared (IR) sensors, motion detection sensors, imagedetection sensors, weight detection sensors, accelerometers, gyrosensors, or other such sensors, for detecting when a user places orremoves a purchasable-unit in or from the user container. In someembodiments, the user container 108 may be located onsite at store 102and may be provided by the store 102 owner or operator user 106 whenuser 106 enters store 102. In some embodiments, the user container 108may also include a transceiver for sending wireless transmissions, suchas Bluetooth standard wireless transmissions, to and from the optimizerdevice 110. In other embodiments, the user container 108 may include awired interface, such as a universal serial bus (USB), to provide awired connection (not shown) for connecting the user container 108 tothe optimizer device 110 to facilitate optimizing onsite user purchasesat a physical location as described herein.

In various embodiments, user 106 may provide user information to thestore 102 owner or operator. For example, in one embodiment user 106 mayenter user information via optimizer device 110 for upload to any of thelocal point-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140. The user information mayinclude information about the user, for example, the user's name, homeaddress, email address, credit score, net worth, or other suchinformation. The user information may also include document basedinformation, including information from the user 106's tax bills, realestate records, utility bills, automobile records, insurance cards,vehicle registrations, and the like. In some embodiments the informationmay be entered via the optimizer device 110, such as via the userinputting the information via a keyboard of the optimizer device 110.

In other embodiments, the user information may be entered via an opticalunit, such as camera or image sensor associated with the optimizerdevice, where the user 106 takes a picture or otherwise scans thedocument such that the user information is sourced and captured directlyfrom the document. For example, the optical sourced user-centricinformation may include information from document(s) such as real estatetax bills, car insurance documents, and the like. This visual processmay verify the validity of the data inputted, where a copy of thedocument, together with its information, may be stored within thecomputer memory of the local point-of-recognition optimizer servers 104or the remote point-of-recognition optimizer servers 140, and can beused for authentication and/or validity purposes. For example,authentication and/or validity can include comparing the optical sourceduser-centric information/document to trusted user information, such asuser credit card information or equivalent information, such that theuser-centric information is verified against trusted information of theuser. Optical sourcing can include the use of object characterrecognition (OCR), where the characters and text of the document areidentified from the optical source and used to digitally create anelectronic version of the information for upload to and storage with anyof the local point-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140. Thus, for example, a displayscreen of the optimizer device 110 may display a user-centricinformation profile as described herein and may be used to inputpersonal data such as real estate tax bills, car insurance documents,and the like. The optical recognition capability of the optimizer device110 may capture the consumer's name and address and may cross-check withconsumer's credit card or equivalent data. In this manner, suchuser-centric information/personal data is verified.

In still further embodiments, user 106 may input the information viaanother device, such as user laptop 120. In such embodiments, the usermay enter or upload information or documents to any of localpoint-of-recognition optimizer servers 104 or remotepoint-of-recognition optimizer servers 140 from user laptop 120 vianetwork 130. The user laptop 120 may be connected to network 130, and,therefore local point-of-recognition optimizer servers 104 and remotepoint-of-recognition optimizer servers 140, via connection 122, whichcan be, for example an Internet connection.

As described herein, the user information may be used to generate orupdate a user-centric information profile. The user information and auser-centric information profile may be stored on and accessed from thecomputer memory and/or databases of local point-of-recognition optimizerservers 104 or remote point-of-recognition optimizer servers 140. Theuser-centric information profile may contain all of the data andinformation uploaded by user 106. The user-centric information profilemay contain a user identifier (ID) that uniquely identifies user 106 inthe point-of-recognition optimizer system. The user-centric informationprofile may also contain a user score or ranking, which ranks the user106 as compared with other users of the system. The user score can bebased on all or a portion of the information provided by the user. Inone embodiment, for example, the user score could indicate the user106's purchasing power, such as the consumer's ability to purchaseproducts or purchasable units, other such purchasing related metrics. Insome embodiments, the user score may be similar to the user's creditscore, such as a credit score conventionally provided by Equifax,Experian, or Transunion.

In various embodiments described herein, one or more competingpurchasable-unit distributors A-C 150 may access the user information orthe user-centric information profile of user 106 over network 130. Thecompeting purchasable-unit distributors A-C 150 may be competingmanufactures or wholesale distributors that provide purchasable-unitproducts to store 102 and/or its customers. The competingpurchasable-unit distributors A-C 150 may be associated with respectiveservers, including each of competing purchasable-unit distributor serverA 152, competing purchasable-unit distributor server B 154, andcompeting purchasable-unit distributor server C 156. As illustrated inthe embodiment of FIG. 1, the competing purchasable-unit distributorservers A-C (152-156) are each connected to network 130 and may receiveand transmit computer transmissions via network 130 to any of the localpoint-of-recognition optimizer servers 104, the remotepoint-of-recognition optimizer servers 140, the user 106 optimizerdevice 110, or the user laptop 120.

Each of the competing purchasable-unit distributor servers A-C (152-156)may include one or more processors and computer memories and mayimplement one or more web-based platforms such as Microsoft ASP.NET,Java Server Pages (JSP), Ruby on Rails, or other such web-basedplatforms in order to receive and respond to computer transmissions vianetwork 130 as described herein.

In some embodiments, user 106 may use the optimizer device 110 toidentify and recognize a purchasable-unit and its relatedpurchasable-unit ID. The point-of-recognition optimizer servers 104 or140 may then determine a plurality of competing purchase-units based onthe recognized purchasable unit's purchasable-unit ID, where each of thecompeting purchase-units correspond to purchasable-units manufacturedby, distributed by, or otherwise provided by each competingpurchasable-unit distributors A-C 150.

Each the competing purchasable-unit distributor servers A-C (152-156)may then each receive, from the point-of-recognition optimizer servers104 and/or 140, the user ID of user 106 and an indication of a competingpurchasable-unit that is manufactured, distributed, or otherwiseprovided by the competing purchasable-unit distributor. Each of thecompeting purchasable-unit distributor servers A-C (152-156) may accessuser 106's user-centric information profile, using the user 106's userID, to receive user 106's user information, user score, or otherinformation that is stored for user 106 at the localpoint-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140. In other embodiments thecompeting purchasable-unit distributor servers A-C (152-156) may eachreceive the user 106's user-centric information profile, which includesthe user ID of user 106 and other information, such that the competingpurchasable-unit distributor servers A-C (152-156) would not need toaccess the local point-of-recognition optimizer servers 104 or theremote point-of-recognition optimizer servers 140 to retrieve the user106's user-centric information profile or related information.

Based on user 106's user-centric information profile and other relatedinformation, each of the competing purchasable-unit distributors A-C 150may bid on the opportunity to send an offer to the user 106's optimizerdevice 110. The offer may be for the distributor's indicated competingpurchasable-unit that is available from the distributor, and may includea discount, coupon, advertisement, incentive, or other message relatedto the respective distributor's competing purchasable-unit. For example,a given bid may be an electronic transmission to the store 102 owner oroperators, such as to the local point of-recognition optimizer servers104 or the remote point-of-recognition optimizer servers 140, and mayinclude a fee-based bid that includes an indication of a fee that arespective competing purchasable-unit distributor A-C 150 (e.g.,competing purchasable-unit distributor A) is willing to pay to the store102 owner or operator for the opportunity of user 106's optimizer device110 to receive an offer (e.g., from the competing purchasable-unitdistributor A).

As described in various embodiments herein, the competingpurchasable-unit distributors A-C 150 (e.g., competing purchasable-unitdistributor A) that outbids the other competing purchasable-unitdistributors A-C 150 (e.g., competing purchasable-unit distributors Band C) is the outbidding distributor and may send the offer to thepoint-of-recognition servers 104 and/or 140, where the offer may berouted and transmitted by the point-of-recognition servers 104 and/or140 to the optimizer device 110 for display to user 106. The user 106may then chose to accept the offer, for example, by inputting anacceptance selection via the optimizer device 110 or by placing theoffered purchasable-unit in the user container 108 as further describedherein. The user 106 may also chose to reject the offer by choosing adifferent purchasable-unit or by not purchasing any purchasable-unit.

FIG. 2 illustrates an embodiment of a consumer-oriented purchasetransaction using the point-of-recognition optimizer systems and methodsdescribed herein. In the embodiment of FIG. 2, consumer 206 correspondsto user 106 and optimizer shopping cart 208 corresponds to usercontainer 108 of FIG. 1, respectively, where each of consumer 206 andoptimizer shopping cart 208 are specific embodiments of user 106 anduser container 108, respectively. Accordingly, the disclosure of FIG. 1for user 106 and user container 108 apply similarly herewith withrespect to FIG. 2.

In the embodiment of FIG. 2 consumer 206 may enter a physical store,such as store 102, and receive the optimizer shopping cart 208. In theembodiment of FIG. 2, the optimizer shopping cart 208 includes anoptimizer device 210. Optimizer device 210 corresponds to the optimizerdevice 110 of FIG. 1, and, accordingly, the disclosure of FIG. 1 for theoptimizer device 110 applies similarly herewith with respect to FIG. 2.In the embodiment of FIG. 2, the optimizer device 210 is an electronicdevice including one or more processors, software, including, forexample, mobile App software (e.g., mobile App software based on AppleiOS, Google Android, or other mobile App platform software), and adisplay screen that may be viewed by consumer 206. The optimizershopping cart 208 also includes one or more sensors that, with respectto embodiment of the optimizer shopping cart 208, create a sensor field220 that is operable to detect when consumer 206 places apurchasable-unit into the shopping cart 208 and is operable to detectwhen consumer 206 removes a purchasable-unit from the shopping cart 208.As described for container unit 108, the sensors of the sensor field 220can include infrared (IR) sensors, motion detection sensors, imagedetection sensors, weight detection sensors, accelerometers, gyrosensors, or other such sensors. In several embodiments, the sensors ofthe sensor field 220 communicate, via wired or wireless transmission,with the optimizer device 210, where the user's placement of apurchasable-unit in the optimizer shopping cart 208 can cause theoptimizer to display a price or other information on the display screenof the optimizer device 210.

In certain embodiments, the point-of-recognition optimizer servers 104and/or 140 may also be configured to communicate with a user container(e.g., shopping cart 208), either directly or indirectly via anoptimizer device (e.g., optimizer device 210). For example, in suchembodiments, the point-of-recognition optimizer servers 104 and/or 140may store instructions, e.g., in computer memory of thepoint-of-recognition optimizer servers 104 and/or 140, foridentification of purchasable-units via related purchasable-unit IDs.One or more processors, as described herein, of the point-of-recognitionoptimizer servers 104 and/or 140 may be configured to execute theinstructions. The point-of-recognition optimizer servers 104 and/or 140,e.g., via their respective one or more processors, may becommunicatively coupled to a wireless transceiver (e.g., wirelesstransceiver 126). For example, the wireless transceiver may send andreceive wireless transmissions using any one or more of the Bluetooth,WiFi, or cellular wireless transmission standards. The wirelesstransceiver (e.g., wireless transceiver 126) may be communicativelycoupled either directly to the point-of-recognition optimizer servers104 and/or 140 or indirectly, e.g., via a network (e.g., network 130,private store network 123, etc.).

A user container (e.g., shopping cart 208) may have one or more sensors(e.g., the sensors of the sensor field 220) and a wireless transceiver(not shown). The wireless transceiver of the user container (e.g.,shopping cart 208) may be configured to send and receive wirelesscomputer transmissions to and from at least one of (1) a wirelesstransceiver of an optimizer device (e.g., optimizer device 210) asassociated with a user, or (2) the wireless transceiver (e.g., wirelesstransceiver 126) of point-of-recognition optimizer servers 104 and/or140. As described herein, the user container is onsite at the physicallocation and may be configured to detect, via the one or more sensors(e.g., the sensors of the sensor field 220), a chosen purchasable-unitwhen the user places the chosen purchasable-unit in the user container(e.g., shopping cart 208). In certain embodiments, the chosenpurchasable-unit may have a plurality of surfaces (e.g., the pluralityof surfaces of product packaging, a box, etc.). In such embodiments, thechosen purchasable-unit ID may be identifiable, via one or more sensors(e.g., the sensors of the sensor field 220), on one or more of theplurality of surfaces. For example, in such embodiments, the one or moresensors may identify the placement of the chosen purchasable-unit in theshopping cart 208 when the chosen purchasable-unit contains an invisibleDigimarc barcode (e.g., as described herein) imprinted one or moresurfaces of the purchasable-unit product packaging. In such embodiments,the purchasable-unit ID may be based on the Digimarc standard orotherwise use the Digimarc based standard, for example, where thepurchasable-unit ID is encoded on the one or more surfaces ofpurchasable-unit product packaging using the Digimarc based standard.

In related embodiments, the chosen purchasable-unit may be associatedwith a radio frequency identification (RFID) tag. In such embodiments,the chosen purchasable-unit ID is identifiable, via one or more sensors(e.g., the sensors of the sensor field 220), from the RFID tag, wherethe RFID tag has encoded the chosen purchasable-unit ID. Thus, in suchembodiments, the one or more sensors (e.g., the sensors of the sensorfield 220) may be arrayed to recognize the RFID tag when a user placesthe chosen purchasable-unit in the shopping cart 208.

In further related embodiments, a user container may include one or moresensor indicators (not shown). The one or more sensor indicators may beconfigured to indicate a status of the one or more sensors (e.g., astatus of the sensors of the sensor field 220). In various aspects, theone or more sensor indicators may be configured to visually or audiblyindicate the status of the one or more sensors (e.g., the sensors of thesensor field 220). For example, the one or more sensor indicators mayvisually emit different color indications, or different audible tones,indicating that the one or more sensors of the user containersuccessfully detected (or failed to detect) a chosen purchasable-unit IDof a chosen a purchasable-unit. Thus, the status of the one or moresensors, e.g., via the visual or audible indicators, may indicate atleast one of a success status or a failure status, where the one or moresensor indicators are configured to indicate the success status orfailure status when the user places the chosen purchasable-unit in theuser container. As an example embodiment of a visual indicator, thenormal color of the sensor indicator(s) (e.g., indicators of the sensorsof the sensor field 220) may be yellow. In such example, when a chosenpurchasable-unit passes through the sensor field 220 and is accuratelyidentified (e.g., the related purchasable-unit ID is detected), thesensor indicator(s) may flash or change to green. In contrast, when achosen purchasable-unit passes through the sensor field and is notaccurately identified, the sensor indicators(s) may flash or change tored. In such case, a user may readjust the position of the chosenpurchasable-unit in the user container (e.g., shopping cart 208) toattempt a successful identification by the sensors (e.g., the sensors ofthe sensor field 220), which may be indicated by the sensor indicator(s)turning green. In embodiments where the product is deposited in the cartwithout proper identification by the sensors (e.g., the sensors of thesensor field 220), the point-of-recognition optimizer servers 104 and/or140 may receive a wireless transmission indicating the error. In suchsituations, the consumer may subsequently need to utilize a manualcheckout procedure (e.g., checkout lane or station in the store 102).

It is to be understood that other indicators, in addition to visual andaudible indicators as described above, may be used instead of, or inaddition to, those described herein.

The one or more processors of the point-of-recognition optimizer servers104 and/or 140 may be configured to receive, via the communicativelycoupled wireless transceiver (e.g., wireless transceiver 126), apurchasable-unit ID associated with the chosen purchasable-unit from atleast one of (1) the wireless transceiver of the optimizer device (e.g.,optimizer device 210), or (2) the wireless transceiver of the usercontainer. Thus, the point-of-recognition optimizer servers 104 and/or140 are able to receive, either directly from the user container (e.g.,shopping cart 208), or indirectly from the optimizer device (e.g.,optimizer device 210), a purchasable-unit ID associated with a chosenpurchasable-unit that the user placed in the user container (e.g.,shopping cart 208).

In some embodiments, the one or more processors of thepoint-of-recognition optimizer servers 104 and/or 140 may be configuredto determine a plurality of competing purchasable-units based on thepurchasable-unit ID. The determination may include the optimizer serversending the purchasable-unit ID to one or more distributor serversassociated with a plurality of competing purchasable-unit distributors.The determination may further include the optimizer server receiving oneor more offers from the one or more distributor servers corresponding tothe plurality of competing purchasable-units, where the plurality ofcompeting purchasable-units are located either onsite at the physicallocation or offsite of the physical location. The one or more processorsof the point-of-recognition optimizer servers 104 and/or 140 maytransmit, via a second computer transmission to the optimizer device(e.g., optimizer device 210), an offer for an offered purchasable-unit.As described herein, the offer may originate from the one or moredistributor servers of an outbidding purchasable-unit distributor, wherethe offer was chosen from the one or more offers such that the one ormore distributor servers of the outbidding purchasable-unit distributoroutbid the one or more distributor servers of all other distributorsfrom the plurality of competing purchasable-unit distributors for anopportunity of the optimizer device to receive the offer.

In some embodiments, the point-of-recognition optimizer servers 104and/or 140 may be configured to determine that an interference thresholdvalue has been passed regarding the user's interaction with the usercontainer (e.g., shopping cart 208). In such embodiments, thepoint-of-recognition optimizer servers 104 and/or 140 may be furtherconfigured to generate, based on the interference threshold value, analert, the alert indicating to onsite personnel at the physical locationto assist the user.

In still further embodiments, the point-of-recognition optimizer servers104 and/or 140 may be configured to initiate a purchase request, basedon the purchasable-unit ID, to purchase the chosen purchasable-unit onbehalf of the user when the user is within a proximity to an exit of thephysical location. In such embodiments, the point-of-recognitionoptimizer servers 104 and/or 140 may update a user-centric informationprofile associated with the user based on the purchase request.

In the embodiment of FIG. 2, consumer 206 may use hand-held productidentifier 202 to identify a purchasable unit identifier (ID) associatedwith purchasable-unit, such as product A 205. The hand-held productidentifier 202 may be a part of the optimizer device 210 thatcommunicates with the remainder of the optimizer device 210 tofacilitate optimizing onsite user purchases as described herein. Forexample, in the embodiment of FIG. 2, the hand-held product identifier202 is a separate electronic device that may communicate with optimizerdevice 210 using wireless communication protocols or standards such asthe Bluetooth standard or the IEEE 802.11 specification protocol. Inanother embodiment, the hand-held product identifier 202 is included aspart of the optimizer device 210 as a single electronic device, whichcan be, for example, any of the tablet device 112, the mobile phone 114,or any other such portable electronic device 116 as shown and describedfor FIG. 1. For example, in an embodiment where the optimizer device 210is the consumer 206 mobile phone, the consumer's mobile phone willinclude both the functionality of the optimizer device 210 and hand-heldproduct identifier 202 as described herein.

Product A 205 may be one of several competing purchasable-unitsavailable onsite at store 102, where product A 205 may be stocked in asimilar physical onsite location, such as storage shelf 207 within store102, with other competing purchasable-unit products distributed by othercompeting purchasable-unit distributors, including competingmanufactures or wholesalers, for example, competing purchasable-unitdistributors A-C 250. In the embodiment of FIG. 2, competingpurchasable-unit distributors A-C 250 are depicted as competingmanufacturers, but that correspond to the competing purchasable-unitdistributors A-C 150 of FIG. 1. Accordingly, the disclosure of FIG. 1for the competing purchasable-unit distributors A-C applies similarlyherewith with respect to FIG. 2.

Each of the competing purchasable-units, including product A 205, suchas those on storage shelf 207, may include a unique purchasable-unit IDthat identifies the respective purchasable-unit, including informationabout the product and/or specific purchasable-unit (e.g., a product IDof the product or serial number of the purchasable-unit). In someembodiments, a purchasable-unit ID may be invisible (e.g., notdiscernible without detailed inspection) and/or embedded over the entireproduct package. For example, in certain embodiments, a purchasable-unitmay have a plurality of surfaces where the purchasable-unit ID isidentifiable to the optimizer device 210/hand-held product identifier202 on each of the plurality surfaces. Consequently, the optimizerdevice 210/hand-held product identifier 202 can identify apurchasable-unit ID simply being pointed at, or by being within avicinity of, the related purchasable-unit. Such embodiments avoid theconventional procedure of having to specifically locate a UPC barcode ona given product.

For example, one example technology, provided from Digimarc Corporation,is a Digimarc barcode that is invisible and embedded over the entirepackaging of a product. Use of the Digimarc barcode, for example, atleast in some embodiments, would allow a purchasable-unit ID to beprovided on a plurality of surfaces of a purchasable-unit, and wouldavoid the need for identification of a UPC barcode.

In the embodiment of FIG. 2, once the purchasable-unit identifier (ID)associated with the purchasable-unit product A 205 has been identifiedby the optimizer device 210/hand-held product identifier 202, thepurchasable-unit identifier (ID) may be received by thepoint-of-recognition optimizer system (not shown) including, forexample, any of the local point-of-recognition optimizer servers 104 orthe remote point-of-recognition optimizer servers 140 of FIG. 1.

The point-of-recognition optimizer servers 104 and/or 140 may thendetermine a plurality of competing purchase-units based on therecognized purchasable unit's purchasable-unit ID. The plurality ofcompeting purchase-units may include the recognized purchasable-unit(e.g., purchasable-unit product A 205) as identified by the optimizerdevice 210/hand-held product identifier 202, and also one or moreadditional purchasable-units. (For example, in one embodiment, the oneor more additional purchasable-units may include the remaining,competing purchasable-units located on storage shelf 207. In anotherembodiment, the one or more additional purchasable-units may includeoffsite, competing purchase-units that are available from offsitedistributors, including manufacturers and/or wholesalers, who do not yethave their purchasable-units in inventory or display at the store 102.For example, in the present embodiment, purchasable-unit distributor C,as shown in FIGS. 1 and 2 may be an offsite distributor. In such anembodiment, the offsite distributors (e.g., such as distributor C) maybe especially incentivized to bid for an opportunity to communicate witha consumer because, for example, offsite distributor C would not have anonsite product or purchasable-unit for the consumer to interact with.The offsite distributor C may, however, benefit by not having topurchase floor or shelf space at store 102 to place purchase-units.Where offsite distributor C is a smaller company or distributor, theoffsite functionally of the optimizer system allows such smallerdistributors to offer competitive prices vis-à-vis larger distributors(e.g., distributor A) who may have purchasable-units onsite.Accordingly, a benefit to offsite distributors may be securing a sizablenumber of consumer sales without having any product at the store 102. Insome instances, the sizable number of consumer sales may lead the ownerof store 102 to decide to display the offsite distributor's product inthe store 102. For example, the store 102 owner may already havebenefitted through fees earned due to higher bids from the offsitedistributor. In addition, consumers may benefit from offers from theoffsite distributor via lower prices and greater variety resulting fromincreased competition from offsite distributors orchestrated by thefeatures and functionality of optimizer systems and methods as describedherein.

In either embodiment, each of the plurality of competing purchase-unitsmay correspond to purchasable-units manufactured by, distributed by, orotherwise provided by each of the competing purchasable-unitdistributors A-C 250, which, as described can be either an onsitedistributor, an offsite distributor, or a distributor that offersproducts in both an onsite and offsite capacity.

The point-of-recognition optimizer servers 104 or 140 may then send toeach of the competing purchasable-unit distributors A-C 250 the user IDof consumer 206 and an indication of the distributor's specificcompeting purchasable-unit that was determined to be a competingpurchasable-unit by the point-of-recognition optimizer servers 104and/or 140. For example, manufacturer A of competing purchasable-unitdistributors A-C 250 would receive an indication that thepurchasable-unit product A 205 was a competing purchasable-unit that wasdetermined to be a competing purchasable-unit by virtue of the consumer206 identifying purchasable-unit product A 205 with the optimizer device210/hand-held product identifier 202. As another example, manufacturer Bof competing purchasable-unit distributors A-C 250 would receive anindication that purchasable-unit product B (not shown, but included onstorage shelf 207) was a competing purchasable-unit that was determinedto be a competing purchasable-unit by virtue of being a similar orcompeting product to purchasable-unit product A 205. Manufacturer Cwould receive similar information.

Using the user's ID, each of the competing purchasable-unit distributorsA-C 250 may access a store database 212 that maintains a history ofconsumer 206's purchases and related personal information. The storedatabase 212 can include consumer 206's user-centric information profileand other user information and may be stored in any of any of the localpoint-of-recognition optimizer servers 104 or the remotepoint-of-recognition optimizer servers 140, as described herein for FIG.1.

Based on the user-centric information profile and/or user informationfrom store database 212, each of the competing purchasable-unitdistributors A-C 250 may bid on the opportunity of the optimizer device210 to receive an offer from the outbidding distributor. In theembodiment of FIG. 2, manufacture A may outbid manufacturers B and C byagreeing to pay an advertising fee to the owner or operators of thephysical store, such as store 102, where consumer 206 is onsite at. Insuch an embodiment, outbidding manufacturer A may send an offer 214,which can include product advertisements, electronic coupons, or otherincentives, to the point-of-recognition optimizer servers 104 and/or140, where the point-of-recognition optimizer servers 104 and/or 140 maytransmit the offer 214 to optimizer device 210 for display to consumer206.

In the embodiment of FIG. 2, the offer 214 may incentivize consumer 206to purchase the purchasable-unit of product A 205. The user can placethe purchasable-unit product A 205 in the optimizer shopping cart 208indicating a desire to purchase purchasable-unit product A 205. As morefully described with respect FIGS. 3 and 4, the sensor field 220 candetect that the purchasable-unit product A 205 has been placed in theoptimizer shopping cart 208 and that consumer 206 has not crossed aninterference threshold value by overly tampering with the placement orarrangement of the purchasable-unit product A 205 within the optimizershopping cart 208.

In the embodiment of FIG. 2, when the user is within a proximity to anexit of the physical location, such as the exit of store 102, thepoint-of-recognition system can begin a consumer purchase procedure 230,which is more fully described in FIG. 3, where the consumer purchaseprocedure 230 includes sending a purchase request, based on the chosenpurchasable-unit ID, so that the consumer 206 may purchase the relatedchosen purchasable-unit.

In some embodiments, the purchase request may cause the consumer 206'spurchase history and/or other user information to be updated with thenew purchase transaction, where the new information is stored, forexample, in store database 212 on the point-of-recognition optimizerservers 104 and/or 140.

FIG. 3 illustrates an embodiment of a consumer purchase procedure 230 inaccordance with the point-of-recognition optimizer systems and methodsdescribed herein. At block 302, a product identifier, such as anoptimizer device 110 or 202/210, may receive a barcode signal (e.g., asignal indicative of the purchasable-unit ID) and may register oridentify a specific product, for example, the recognizedpurchasable-unit identified with the barcode signal. The barcode signalcan be received by the optimizer device 110 or 202/210 where theoptimizer device 110 or optimizer device 202/210 identifies thepurchasable-unit ID via infrared (IR), radio frequency, or imagescanning technology.

At block 304, product information may be transmitted to thepoint-of-recognition optimizer system. For example, as described herein,the product information may include information about the recognizedpurchasable-unit, including the purchasable-unit ID of the recognizedpurchasable-unit. Such information can be transmitted to thepoint-of-recognition optimizer servers 104 and/or 140, where adetermination of a plurality of competing purchasable-units can be madebased on the purchasable-unit ID.

At block 306, the flow of user and specific product information may besent to the competing purchasable-unit distributors (e.g.,manufacturers) regarding specific products. For example, this can be thecompeting purchasable-unit distributors A-C of FIGS. 1 and 2. Asdescribed herein, each of the competing purchasable-unit distributorsA-C can receive the user and specific product information, including theuser's user ID, user-centric information profile, and competing productinformation available of the respective distributor, where suchinformation can be used by each of the distributors A-C to generaterespective bids for the opportunity of the user's optimizer device toreceive an offer associated with an offered purchasable-unit from thewinning, outbidding distributor. The outbidding distributor may transmitthe offer to the point-of-recognition optimizer servers 104 and/or 140for transmission to the consumer's optimizer device for display.

At block 308, the consumer, such as consumer 206 of FIG. 2, makes his orher purchase decision by either selecting to purchase the offeredpurchasable-unit (e.g., product A 205 of FIG. 2) or one of the othercompeting purchasable-units, such the additional competing units ofstorage shelf 207 of FIG. 2 or competing offsite purchasable-units. Theconsumer 206, may also determine not to buy (310) any of thepurchasable-units.

At block 312, once the consumer decides to buy one or more of thecompeting purchasable-units, i.e., the chosen purchasable-units, thepoint-of-recognition system may determine whether the chosenpurchasable-units are available (314) onsite at the physical location,for example, at store 102.

If a chosen purchasable-unit is not available (316), then, at block 318,the consumer can elect to have the chosen purchasable-unit delivered toa location specified in a personal information store database, such asstore database 212. For example, the chosen purchasable-unit may not beavailable onsite (e.g., only available via an offsite distributor) orthe chosen purchasable-unit may not be in stock (e.g., where an onsitedistributor is out of stock of the chosen purchasable-unit) at thephysical location, and the consumer may purchase the chosenpurchasable-unit, via the optimizer device, for delivery of the chosenpurchasable-unit to the user's home address. The specified address mayhave been previously stored in the store database 212 so that theconsumer need not enter the information again. In another embodiment,the consumer may choose to pick up the chosen purchasable-unit at thestore, e.g., after receiving a notification email or text that thechosen purchasable-unit is available for pick up. In some embodiments,the store-pickup alternative option may reduce the price of the chosenpurchasable-unit because the distributor's delivery cost may decline asmultiple products may be batch delivered to a single location, e.g.,store 102. The user may choose the delivery option at time of purchase,where the optimizer device may change the price depending on thedelivery option chosen (e.g., a reduced price when the user selects thestore-pickup option).

If instead the chosen purchasable-units are available (320), then atblock 322, the chosen purchasable-units may be detected by the user'soptimizer shopping cart, such as optimizer shopping cart 208, afterpassing through the cart's sensor field 220. For example, in someembodiments, the chosen purchasable-units may be detected with one ormore sensors associated with a user container (e.g., the optimizershopping cart 208) when the consumer places the chosen purchasable-unitin the user container. As described herein, the user container can be,for example, a shopping cart, shopping basket, or other device orapparatus for detecting purchasable-units chosen by the user.

At block 324, the number and identity of the chosen purchasable-unitsmay be recorded in the optimizer device's software, such that theoptimizer device (e.g., 202/210) may then determine the total number andtype of chosen purchasable-units and compute and display a total priceand other purchase-related information including, for example, sales taxinformation, discount information received from any related offer, orother such purchase-related information on the display screen of theoptimizer device 202/210. For example, in some embodiments, the displayscreen of the optimizer device (e.g., 202/210) may maintain an updatedtotal of the cost of purchasable-units deposited in the shopping cart,e.g., including money or costs the user saved due to the user acceptingoffers (e.g., with favorable prices) associated with purchasable-unitsfrom the purchasable-unit distributors or manufacturers (e.g., theoutbidding purchasable-unit distributor) as described herein. Forexample, the optimizer device 202/210 may display an updated costsavings value based on an offer from an outbidding purchasable-unitdistributor. In still further embodiments, if a purchasable-unit isremoved from the cart, the optimizer device may display a recalculatedcurrent total reflecting the removal of the purchasable-unit, which mayreflect the user no longer accepting the offer. With this graphicaldisplay, the consumer is informed during the shopping experience of thecost savings achieved through the use of the optimizer platform and theoffers it provides as described herein. In some embodiments, theconsumer may purchase the one or more chosen purchasable-units (e.g.,purchasable-units remaining in a user container (e.g., shopping cart) bysimply exiting the store, as further described in FIG. 4.

At block 326, a display screen of the optimizer device may display awarning if the consumer interferes with normal operation of the usercontainer's sensor field. For example, in some embodiments, theoptimizer device may be configured to determine that an interferencethreshold value has been passed regarding the user's interaction withthe user container, and generate, based on the interference thresholdvalue, an alert that indicates to onsite personnel to assist the user.In such an event, store personnel, such as employees of store 102, maybe required to manually input purchase data related to the consumer'schosen purchasable-units into the optimizer device.

FIG. 4 illustrates an embodiment of a checkout process 400 in accordancewith the point-of-recognition optimizer systems and methods describedherein. At block 402, a user, such as user 106 or consumer 206, hascompleted his or her shopping experience at store 102. This can include,for example, after the user has placed his or her chosenpurchasable-units in the user container, such as optimizer shopping cart208.

At block 404, the user container's sensors, such as the sensor field 220of the optimizer shopping cart 208, can detect whether the consumer 206passed a threshold value of interference with respect to normaloperation of optimizer shopping cart 208. For example, in oneembodiment, the sensor field 220 can detect whether the consumer hasmoved a purchasable-unit in and out of the optimizer shopping cart 208more than allowed by an interference threshold value.

If the consumer has passed the threshold value of interference, then, atblock 406, store personnel, such as employees of store 102, may bealerted to manually input purchase information into the optimizerdevice. The threshold value of interference benefits the owner oroperator of the store 102 by deterring theft and also by assistingconsumers that need help operating the optimizer device, optimizershopping cart, or otherwise.

At block 410, after the store personnel assist the consumer, or afterthe consumer has successfully placed a chosen purchasable unit in theoptimizer shopping cart 208 without passing the interference thresholdvalue, the point-of-recognition optimizer system, for example, any ofthe point-of-recognition optimizer servers 104 and/or 140, may receive apurchase request from the optimizer device and may process payment andorganize the purchase data using the consumer's identification. Incertain embodiments, the optimizer device can initiate a purchaserequest, based on the chosen purchasable-unit identifier (ID), topurchase the chosen purchasable-unit when the user is within a proximityto an exit of the physical location. For example, after shopping iscompleted, the consumer may pass a store exit point and avoid thetime-consuming conventional checkout process. At that time, theconsumer's selected payment option (e.g., pay via a particular creditcard), where the option and credit card information may be stored in theconsumer's user-centric information profile, may be used to pay for thechosen purchasable-unit in the optimizer shopping cart 208.

At block 412, the point-of-recognition optimizer system, for example,any of the point-of-recognition optimizer servers 104 and/or 140, maycause the execution of print command to print a paper receipt at aprinter located onsite at the store, such as store 102, and for whichthe consumer can retrieve upon exiting the store. In another embodiment,the point-of-recognition optimizer servers 104 and/or 140 can send anelectronic receipt to the consumer, such as an email to the consumer'semail address indicated in the consumer's user-centric informationprofile, where the email may include information about the purchase.

At block 414, the point-of-recognition optimizer system, for example,via the optimizer device, may update a store database to update thehistory of consumer's purchases with the recent purchase. In certainembodiments, based on the purchase request, the optimizer device cancause an update to a user-centric information profile associated withthe user. For example, the purchase transaction for the chosenpurchasable-unit may be used to update the consumer's purchase historymaintained via the store database 212.

At block 416, at least in one embodiment, the consumer may exit thestore with the optimizer shopping cart 208 containing the purchaseditems, which may include the chosen purchasable-units that the consumerplaced in the optimizer shopping cart 208 and that were purchased whenthe consumer passed within the proximity to the exit of the physicalstore (e.g., store 102) and that triggered the purchase request. In someembodiments, the consumer may leave the optimizer shopping cart 208 at adesignated location onsite at the store 102 for a next consumer.

FIG. 5 illustrates an embodiment of method 500 for optimizing userpurchases at a physical location in accordance with the disclosuresherein. At block 502, a product identifier, such as the optimizer device110 or optimizer device 202/210, registers or identifies a specificproduct, for example, the recognized purchasable-unit identified withthe optimizer device 110 or optimizer device 202/210 as describedherein. For example, a recognized purchasable-unit and its relatedpurchasable-unit ID may be identified or recognized by an optimizerdevice associated with a consumer or user. In various embodiments, therecognized purchasable-unit can be located onsite (e.g., at store 102)with the optimizer device, so that a consumer or user can use theoptimizer device to identify the recognized purchasable-unit and itsrelated purchasable-unit ID. For example, as described for FIG. 2, theconsumer may use a hand-held product recognizer to point at apurchasable-unit of a product such as Product A205.

In certain embodiments, the purchasable-unit may have a plurality ofsurfaces and the purchasable-unit ID may be identifiable to theoptimizer device on each of the plurality surfaces as described herein.For example, in the embodiment of FIG. 2, the Digimarc barcode orsimilar invisible indicator on the surfaces of Product A 205 isrecognized and the related purchasable-unit information is transmittedand stored in the optimizer device 210.

In some embodiments, the optimizer device may be a mobile phone or otherportable electronic device, such as a tablet, of the user as describedfor FIG. 1. A store owner or operator may benefit from lower operatingcosts when the user uses his or her own device as the optimizer becausethe store owner of operator would not be required to purchase andmaintain at least the hardware portion of the optimizer device. In suchan embodiment, the user's phone could be updated with mobile Appsoftware running an Optimizer App as described herein.

In other embodiments, the optimizer device may be a device provided bythe owner or operator of the physical location. For example, the usermay be provided an optimizer device as the user enters the store, wherethe optimizer device may include two separate devices including ahand-held product identifier device for identifying purchasable unitsand a display screen device for displaying offers as described for FIG.2.

At block 504, the distributor (e.g., manufacturer) of the products ofthe recognized purchasable unit and any competing distributors (e.g.,manufacturers) are identified. For example, in various embodiments, thepoint-of-recognition optimizer system, including thepoint-of-recognition optimizer servers 104 and/or 140, may be configuredto receive, via a computer transmission, a purchasable-unit identifier(ID) associated with the purchasable-unit. The purchasable-unit, may be,for example, a unit of a retail product located onsite at a physicalstore, where the purchasable-unit ID may uniquely identify the unitand/or retail product. The purchasable-unit ID may be used to determinea plurality of competing purchasable-units. The plurality of competingpurchasable-units may include the recognized purchasable-unit identifiedby the optimizer device and one or more additional purchasable-unitsoffered by competing manufacturers, wholesalers, or other distributors.For example, the one or more additional purchasable-units may includeone or more offsite, competing purchasable units that are only availablevia offsite distributors as described herein.

In various embodiments, the plurality of competing purchasable-unitdistributors can include one or more product manufacturers and one ormore product wholesalers who distributed the corresponding competingpurchasable-units to a physical store for onsite identification andselection by consumers. The purchasable-units may be a productsmanufactured and/or distributed by a particular manufacturer to thephysical store and made available to consumers onsite. In otherembodiments, the purchasable-units may be products distributed on behalfof a manufacturer by a product wholesaler to the physical store, and,therefore available to consumers onsite. In still further embodiments,the plurality of competing purchasable-unit distributors can include oneor more offsite product manufacturers and/or one or more offsite productwholesalers who distribute corresponding competing purchasable-units toconsumers directly or via the store, such as store 102, for in-storepick up after a consumer has made a purchase via the optimizer systemand methods as disclosed herein.

At block 506, in one embodiment, information about the consumer, such asthe consumer's user-centric information profile and related userinformation stored in store database (e.g., store database 212) asdescribed herein, are sent to all distributors' (e.g., manufacturers')computer servers. As shown for FIG. 2, the competing purchasable-unitdistributions or manufacturers of competing products (e.g., distributorsB and C) plus distributor A are each prompted, for example, via acomputer transmission on network 130, about this specific consumer'sinterest in product A. Each distributor may also receive informationabout the consumer's past purchases plus voluntarily-disclosed personaldata which may include the user-centric information profile and relateduser information. Competing purchasable-unit distributors ormanufacturers may receive or access a consumer's short term (e.g.,consumer's current interest in product A) or long-term purchase history(e.g., past purchases) from a consumer's user-centric informationprofile and/or related user information, e.g., which may be stored indatabase (e.g., store database 212) as described herein. For example,competing purchasable-unit distributors or manufacturers may receive oraccess a consumer's long-term purchase history of the consumer'suser-centric profile information when the consumer identifies a specificpurchasable-unit (e.g., of product A, and related purchasable-unit ID)with the optimizer device 202/210 and makes purchases over time. Inaddition, the competing purchasable-unit distributors or manufacturersmay also receive or access the consumer's short-term purchase history ofthe user's user-centric profile information that includes a consumer'sshort term history (e.g., prior 30 minutes, etc.) of the consumer'sactivity or interest in recent in-store purchasable-units as reflectedby the consumer using, or otherwise identifying with, the optimizerdevice 202/210 various purchasable-unit(s) in the store (e.g., forproduct A).

As described above, in certain embodiments, each of the competingpurchasable-unit distributors can access the user-centric informationprofile or other related information from the point-of-recognitionoptimizer servers 104 and/or 140. The competing purchasable-unitdistributors can use the user-centric information profile or otherrelated information to compete in bidding and/or generate offers tousers as described herein.

In further embodiments, the point-of-recognition optimizer servers 104and/or 140 may implement machine learning algorithms that generatemachine learning based models (e.g., an optimizer machine learningmodel) based on the user-centric information profile or other relatedinformation. For example, the optimizer machine learning model may begenerated (e.g., “trained”) using a supervised or unsupervised machinelearning program or algorithm. The machine learning program or algorithmmay employ a neural network, which may be a convolutional neuralnetwork, a deep learning neural network, or a combined learning moduleor program that learns in two or more features or feature datasets in aparticular areas of interest. The machine learning programs oralgorithms may also include natural language processing, semanticanalysis, automatic reasoning, regression analysis, support vectormachine (SVM) analysis, decision tree analysis, random forest analysis,K-Nearest neighbor analysis, naïve Bayes analysis, clustering,reinforcement learning, and/or other machine learning algorithms and/ortechniques. Machine learning may involve identifying and recognizingpatterns in existing data (such as purchasable-units/products that areroutinely purchased together by consumers or users using the optimizerdevice 202/210 or otherwise, or purchasable-units/products that aretypically purchased by certain users having or being associated withcertain regions, net worth, demographics, etc. as reflected inuser-centric information profile and/or other related information acrossvarious users) in order to facilitate making predictions for subsequentdata (to predict whether a certain user would select an offer from anoutbidding purchasable-unit distributor or otherwise engage in apurchase of a purchasable-unit/product from an purchasable-unitdistributor or manufacturer, etc.).

Machine learning model(s), such as the optimizer machine learning model,may be created and trained based upon example (e.g., “training data,”)inputs or data (which may be termed “features” and “labels”) in order tomake valid and reliable predictions for new inputs, such as testinglevel or production level data or inputs. In supervised machinelearning, a machine learning program operating on a server, computingdevice, or otherwise processor(s) (e.g., the point-of-recognitionoptimizer servers 104 and/or 140), may be provided with example inputs(e.g., “features”) and their associated, or observed, outputs (e.g.,“labels”) in order for the machine learning program or algorithm todetermine or discover rules, relationships, or otherwise machinelearning “models” that map such inputs (e.g., “features”) to the outputs(e.g., labels), for example, by determining and/or assigning weights orother metrics to the model across its various feature categories. Suchrules, relationships, or otherwise models may then be providedsubsequent inputs in order for the model, executing on the server,computing device, or otherwise processor(s) (e.g., thepoint-of-recognition optimizer servers 104 and/or 140), to predict,based on the discovered rules, relationships, or model, an expectedoutput.

In unsupervised machine learning, the server, computing device, orotherwise processor(s) (e.g., the point-of-recognition optimizer servers104 and/or 140), may be required to find its own structure in unlabeledexample inputs, where, for example multiple training iterations areexecuted by the server, computing device, or otherwise processor(s) totrain multiple generations of models until a satisfactory model, e.g., amodel that provides sufficient prediction accuracy when given test levelor production level data or inputs, is generated. The disclosures hereinmay use one or both of such supervised or unsupervised machine learningtechniques.

For example, the point-of-recognition optimizer servers 104 and/or 140may use a consumer's purchase history information from the user-centricinformation profile or other related information of the consumer asfeatures to train the optimizer machine learning model against labelsthat may include new, competing, different, similar, or samepurchasable-units/products, that may represent new offers or purchasetransactions that the user is expected to engage in with respect torelated purchasable-units/products. Thus, the optimizer machine learningmodel may be trained with user-centric profile information or otherrelated information to assist the purchasable-unit distributors ormanufacturers in determining, e.g., detailed information related tooffers or advertisements, or price reduction offers related topurchasable-units/products, as described herein, which could betransmitted to the consumer's display screen of an optimizer device(e.g., optimizer device optimizer device 202/210). Thus, the optimizermachine learning model may provide a unique, context-specific, and/ortime-sensitive aggregation of user-centric profile information or otherrelated information that is unavailable to search engines, such asGoogle, such that the optimizer machine learning model offerssignificant value to purchasable-unit distributors or manufacturers,which, in turn, may generate higher revenues to the store 102 owner oroptimizer platform operator through increased sales ofpurchasable-units/products as described herein.

In some embodiments, the output as generated by the optimizer machinelearning model may be a user action score that defines a probability ofthe user to engage in a purchase associated with one or morepurchasable-units/products as described herein. The output (e.g., theuser action score) may be transmitted to the purchasable-unitdistributors or manufacturers from the recognition optimizer servers 104and/or 140. In addition, or alternatively, the output (e.g., the useraction score) may be accessible by the purchasable-unit distributors ormanufacturers at the recognition optimizer servers 104 and/or 140. Thus,in various embodiments, an optimizer machine learning model may begenerated (e.g., “trained”) based on user-centric profile information ofone or more users, and the purchasable-unit distributors ormanufacturers (e.g., the outbidding purchasable-unit distributordescribed herein) may receive or access the user action score generatedfrom the machine learning model. The user action score may be used by apurchasable-unit distributor or manufacturer to determine whether tocompete in bidding with the other purchasable-unit distributors ormanufacturers as described herein, to determine how and/or to whatdegree to generate an offer to a user/consumer (e.g., such as the amountof the discount to include in the offer) for one or morepurchasable-units/products, or to otherwise facilitate the optimizationof user purchases of purchasable-units/products as described herein.

In either embodiment, at block 508, the user-centric information profileand related user information may be provided or accessed from the storedatabase, such as store database 212, that includes purchase history forthe consumer plus any voluntarily provided personal data. In stillfurther embodiments, at block 510, other databases with a history of theconsumer's purchases may be accessed. The other databases may beprovided by third parties, including, for example, third parties thatutilize their own point-of recognition system, point-of-sale system,online system, or other such systems that maintain user purchasehistory.

As described herein, the user-centric information profile may includedetails about the user's past purchase history or other personalinformation to allow the distributors to generate informed anddata-driven offers or advertisements targeted to the user. The userinformation may have been input via the optimizer device 110, 210 (or,in some embodiments, user laptop 120), where the user information isused to generate or update the user's user-center information profile.

In one embodiment, for example, the optimizer device may include adisplay unit and an input unit. The display unit, such as a displayscreen of a mobile device, may be operable to display the offer and theuser-centric information to the user. The input unit, such as a keyboardor keypad of a mobile device, may be operable to receive user-centricinformation for generating or further updating the user-centricinformation profile. For example, with the optimizer device 110, 210(or, in some embodiments, user laptop 120) a consumer can enterverifiable and up-to-date information about the consumer's automobileownership (insurance cards, state registration documents). Accordingly,the make, model, and age of the consumer's cars may become part ofconsumer's user-centric information profile. Additional information mayalso be downloaded directly from automobile manufacturers and localfirms selling cars to further enhance the consumer's user-centricinformation profile. By analyzing the user-centric information profileor relate user information, the competing purchasable-unit distributorsmay therefore assess the attractiveness of the consumer. Suchinformation, combined with the consumer's purchase history data, enablesthe competing purchasable-unit distributors to better quantify how muchto bid for the opportunity to present offers (e.g.,advertisements/coupons) to the user's optimizer device and how valuableto make the coupons or other incentives to the consumer. As aconsequence, the consumer benefits from competition among competingpurchasable-unit distributors.

The input unit may also be operable to receive login information foractivating the optimizer device in order to protect the user'suser-centric information or preclude other, non-authorized, users frommaking purchases on the user's behalf. For example, in one embodiment,only after logging in with a name and password, will the screendisplayed on the optimizer device show the consumer the status of theconsumer's voluntarily-inputted personal data.

In other embodiments, the optimizer device may include an optical unit,such as a camera, sensor, or scanning device, that the user can use totake digital images or source other such optical information fromdocuments including, for example, the user's tax bills, utility bills,or other such records, and use that optical information to generate orupdate the user-centric information profile. For example, such opticalinformation may be inputted via a camera option on the optimizer devicedisplay screen. In one embodiment, the user may take a picture of his orher automobile registrations, tax bills, or other information, etc.,where such information may be uploaded to the point-of-recognitionoptimizer servers 104 and/or 140 and used to generate or update theuser-centric information profile.

At block 512, each of the competing purchasable-unit distributors maysubmit bids to the point-of-recognition optimizer servers 104 and/or 140and offers for communication to the consumer via the optimizer device.For example, in some embodiments, prior to submitting the bids andoffers to the point-of-recognition optimizer servers 104 and/or 140,each of the competing purchasable-unit distributors may receive theconsumer's user-centric profile information and/or other userinformation. Each competing purchasable-unit distributors would thengenerate bids and offers for transmission back to thepoint-of-recognition optimizer servers 104 and/or 140. Thepoint-of-recognition optimizer servers 104 and/or 140 may then determinethe winning bid and route the offer of the winning bid (e.g., theoutbidding distributor's offer) to the user's optimizer device.Accordingly, once the user information is received, the competingpurchasable-unit distributors' computers would use algorithms toinstantaneously bid for the opportunity to deliver an offer, which caninclude an advertisement and/or electronic coupon, to the optimizerdevice's display screen. The offer may be generated based on theuser-centric information profile including based on such characteristicsincluding, for example, whether the consumer is high net worthindividual, a new consumer, an existing consumer. For example, a highnet worth consumer, who may be a new customer of the particulardistributor or product, may receive a large discount offer in order towin brand loyalty and thus future purchases from the consumer. Asanother example, a high net worth consumer, who is an existing customer,may receive a normal discount since the consumer already may makefrequent purchases of the related product.

In various embodiments, the particular distributor that gets to displayits offer on the optimizer device may have outbid other distributors whocompeted for the opportunity for the optimizer device, and therefore theuser, to receive the offer. For example, the outbidding purchasable-unitdistributor is the distributor that wins the bidding process andtherefore is able to generate the offer received by the optimizerdevice, and, therefore the user. The outbidding purchasable-unitdistributor may have been chosen from the plurality of competingpurchasable-unit distributors, where each of the plurality of competingpurchasable-unit distributors, including the outbidding purchasable-unitdistributor, may correspond to a respective plurality of competingpurchasable-units. The plurality of competing purchasable-units mayinclude the recognized purchasable-unit, the offered purchasable unit,and any remaining purchase-units have similar or competing features.

In one embodiment, the plurality of competing purchasable-units caninclude a same type of purchasable-unit, where the purchasable-units arecommodities or near-commodities to one another. In other embodiment, forexample, the plurality of competing purchasable-units can include two ormore purchasable-units of different product types, where the productsare not commodities, but instead provide competing features or that canbe used in a manner such that a consumer could choose one product overthe other.

In some embodiments, the offered purchasable-unit may be the same as therecognized purchasable-unit. For example, in such embodiments, the offermay originate from the distributor of the recognized purchasable-unit,where the distributor of the recognized purchasable-unit outbid allother distributors for the opportunity of the optimizer device toreceive the offer as described herein.

In other embodiments, the offered purchasable-unit is different from therecognized purchasable-unit. For example, in such embodiments, the offermay originate from a new distributor, different from the distributor ofthe recognized purchasable-unit, where the new distributor outbid allother distributors, including the distributor of the recognizedpurchasable-unit, for the opportunity of the optimizer device to receivethe offer as described herein.

In one embodiment, the outbidding purchase-unit distributor may be thedistributor that agrees to pay a highest distributor fee, with respectto each of the other distributors, to the owner or operator of thestore, such as store 102, for the opportunity of the user's optimizerdevice to receive the offer as described herein. Accordingly, thehighest bidder may be rewarded the opportunity and may pay a fee to thestore owner or operator, which may be ultimately tied to whether theconsumer subsequently buys their product.

In other embodiments, the outbidding purchase-unit distributor may alsobe the distributor that submits or bids a best value to the user orconsumer, such that the offer includes an offer value that is mostfavorable to the consumer. In such embodiments, the offer value may bebased on the user's user-centric information. For example, the value ofcoupons offered to consumers may be tied to their purchase histories andpersonal data, including verifiable data, accessible by the optimizerdevice as described herein.

At block 514, the point-of-recognition optimizer servers 104 and/or 140sends a second computer transmission regarding the competingpurchasable-unit distributor (e.g., manufacturer) having the winning bidand transmits the related offer (e.g., the advertisement and/or coupon)to consumer's optimizer display screen. Thus, the point-of-recognitionoptimizer system can transmit, via a second computer transmission, theoffer for an offered purchasable-unit to the optimizer device, where theuser can view the offer on the optimizer device. As described herein,the offer may originate from the outbidding purchasable-unitdistributor.

At block 516, the consumer may make a purchase decision, which mayinclude placing a chosen purchasable-unit into a user container, such asoptimizer shopping cart 208 as described herein. In various embodiments,the optimizer device may be used to facilitate a user purchase. Forexample, in some embodiments the optimizer device may be configured todetect that a chosen purchasable-unit has been associated with the user.The chosen purchasable-unit can be a purchasable-unit that the userchoses to purchase, which can be any of the recognized purchasable-unit,the offered purchasable-unit, any of the other plurality of competingpurchasable-units onsite at the physical location, or any otherplurality of competing offsite purchase-units that are only availablevia offsite distributors as described herein. In any event, the chosenpurchasable unit can be identified with a chosen purchasable-unitidentifier (ID) that uniquely identifies the chosen purchasable-unitidentifier (ID). For example, the chosen purchasable-units that aredeposited by a consumer in his or her optimizer shopping cart 208 may bedetected by the sensor field 220 via the purchasable-unit identifier(ID) such that the optimizer device may record the price and numberpurchasable-units for purchase.

In certain embodiments, a the optimizer system and methods may involveonly offsite purchasable-units, for example, where the user may interactwith the optimizer device to purchase offsite purchasable unitsdisplayed on the optimizer device and receive competing offers fromcompeting purchasable-unit distributors as described herein. In oneexample embodiment, the user may identify a recognized offsitepurchasable-unit displayed on, and available for purchase via, theoptimizer device. In such embodiments, the consumer may either be onsiteor offsite the physical store location. The point-of-recognitionoptimizer servers 104 and/or 140 may receive, via a computertransmission from the optimizer device, a purchasable-unit identifier(ID) associated with the recognized, offsite purchasable-unit. Thepoint-of-recognition optimizer servers 104 and/or 140 may then determinea plurality of competing purchasable-units based on the purchasable-unitID, where the plurality of competing purchasable-units includes therecognized purchasable-unit and one or more additionalpurchasable-units. The additional purchasable-units may bepurchasable-units offered from offsite distributors, where theadditional purchasable-units are offsite from the user's currentlocation. The point-of-recognition optimizer servers 104 and/or 140 maythen transmit, via a second computer transmission to the optimizerdevice, an offer for an offered purchasable-unit, where the offeroriginates from an outbidding purchasable-unit distributor. As describedherein, the outbidding purchasable-unit distributor may be chosen from aplurality of competing purchasable-unit distributors and the pluralityof competing purchasable-unit distributors may correspond to theplurality of competing purchasable-units, where the outbiddingpurchasable-unit distributor outbid all other distributors from theplurality of competing purchasable-unit distributors for an opportunityof the optimizer device to receive the offer. Accordingly, in suchembodiments, the optimizer systems and methods, where competing, offsitepurchasable-unit distributors compete for and bid for a consumer'sbusiness, can be made available to wholly online sources, for example,online web stores and websites.

At block 518, the point-of-recognition system, such as thepoint-of-recognition servers 104 and/or 140, may receive feedback aboutpurchase decision. The feedback can include the number and prices aboutthe purchasable-units that the consumer purchases. The feedback can beused to update the user's user-centric information profile as describedherein.

At a point in time of the user shopping experience, e.g., after checkoutis completed, the optimizer device may prompt the consumer thatadditional advertisements/coupons are available for viewing that may beunrelated to specific products on store shelves, but potentially ofinterest and valuable to the consumer.

At block 520, where the consumer purchased at least onepurchasable-unit, payment for the purchasable-unit(s) may be sent to thefirm owning or operating store, such as store 102.

ADDITIONAL CONSIDERATIONS

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

What is claimed is:
 1. A point-of-recognition optimizer systemconfigured to optimize user purchases at a physical location, thepoint-of-recognition optimizer system comprising: a computer memoryoperating to store instructions to optimize user purchases at thephysical location; a processor operating to execute the instructions;and an optimizer machine learning model trained with user-centric data,the optimizer machine learning model communicatively coupled to theprocessor, the processor and the computer memory comprising an optimizerserver, the processor executing the instructions on the optimizer serverto: (a) receive at the optimizer server, via a computer transmissionfrom an optimizer device associated with a user, a purchasable-unitidentifier (ID) associated with a recognized purchasable-unit, thepurchasable-unit ID identified by the optimizer device, wherein therecognized purchasable-unit is located onsite at the physical locationwith the optimizer device, (b) generate, by the optimizer machinelearning model, a user action score defining a probability of the userto engage in a purchase of one or more of purchasable-units associatedwith the purchasable-unit ID, (c) transmit, from the optimizer server,the user action score to one or more distributor servers associated withone or more competing purchasable-unit distributors, the optimizerserver configured to receive, from the one or more distributor servers,one or more offers corresponding to the one or more ofpurchasable-units, the one or more purchasable-units located eitheronsite at the physical location or offsite of the physical location, and(d) transmit from the optimizer server, via a second computertransmission to the optimizer device, an offer of an offeredpurchasable-unit, the offer originating from the one or more distributorservers of an outbidding purchasable-unit distributor, the offer chosenfrom the one or more offers, wherein the one or more distributor serversof the outbidding purchasable-unit distributor outbid the one or moredistributor servers of all other distributors of the competingpurchasable-unit distributors for an opportunity of the optimizer deviceto receive the offer.
 2. The point-of-recognition optimizer system ofclaim 1, wherein the user is associated with a user-centric informationprofile having user-centric information, the user-centric informationincluding purchase history of the user.
 3. The point-of-recognitionoptimizer system of claim 2, wherein the processor is configured toenable the outbidding purchasable-unit distributor to receive or accessthe user-centric information associated with the user to generate theoffer.
 4. The point-of-recognition optimizer system of claim 2, whereinthe user-centric information comprises at least portion of theuser-centric data used to train the optimizer machine learning model. 5.The point-of-recognition optimizer system of claim 1, wherein the one ormore of purchasable-units include the recognized purchasable-unit andone or more competing purchasable-units.
 6. The point-of-recognitionoptimizer system of claim 1, wherein the optimizer device displays anupdated cost savings value based on the offer from the outbiddingpurchasable-unit distributor.
 7. The point-of-recognition optimizersystem of claim 1, wherein the offer includes an offer value, andwherein the offer value is based on the user action score.
 8. Thepoint-of-recognition optimizer system of claim 2, wherein the optimizerdevice includes an optical unit, wherein the user-centric informationprofile is further updated with additional information sourced from theoptical unit.
 9. The point-of-recognition optimizer system of claim 2,wherein the optimizer device includes a display unit and an input unit,wherein the display unit is operable to display the offer and theuser-centric information to the user, and wherein the input unit isoperable to receive login information for activating the optimizerdevice and receive user-centric information for further updating theuser-centric information profile.
 10. The point-of-recognition optimizersystem of claim 2, wherein the user-centric information is verifiedagainst trusted information of the user.
 11. The point-of-recognitionoptimizer system of claim 1, wherein the recognized purchasable-unit hasa plurality of surfaces, and wherein the recognized purchasable-unit IDis identifiable on one or more of the plurality of surfaces.
 12. Thepoint-of-recognition optimizer system of claim 1, wherein thepurchasable-unit ID is a Digimarc based purchasable-unit ID.
 13. Thepoint-of-recognition optimizer system of claim 11, wherein therecognized purchasable-unit is associated with a radio frequencyidentification (RFID) tag, and wherein the recognized purchasable-unitID is identifiable from the RFID tag.
 14. A point-of-recognitionoptimizer method for optimizing user purchases at a physical location,the point-of-recognition optimizer method comprising: receiving at anoptimizer server, via a computer transmission from an optimizer deviceassociated with a user, a purchasable-unit identifier (ID) associatedwith a recognized purchasable-unit, the purchasable-unit ID identifiedby the optimizer device, wherein the recognized purchasable-unit islocated onsite at the physical location with the optimizer device;generating, by an optimizer machine learning model communicativelycoupled to the optimizer server, a user action score defining aprobability of the user to engage in a purchase of one or more ofpurchasable-units associated with the purchasable-unit ID, the optimizermachine learning model trained with user-centric data; transmitting,from the optimizer server, the user action score to one or moredistributor servers associated with one or more competingpurchasable-unit distributors, the optimizer server configured toreceive, from the one or more distributor servers, one or more offerscorresponding to the one or more of purchasable-units, the one or morepurchasable-units located either onsite at the physical location oroffsite of the physical location; and transmitting from the optimizerserver, via a second computer transmission to the optimizer device, anoffer of an offered purchasable-unit, the offer originating from the oneor more distributor servers of an outbidding purchasable-unitdistributor, the offer chosen from the one or more offers, wherein theone or more distributor servers of the outbidding purchasable-unitdistributor outbid the one or more distributor servers of all otherdistributors of the competing purchasable-unit distributors for anopportunity of the optimizer device to receive the offer.
 15. Thepoint-of-recognition optimizer method of claim 14, wherein the user isassociated with a user-centric information profile having user-centricinformation, the user-centric information including purchase history ofthe user.
 16. The point-of-recognition optimizer method of claim 15,wherein the optimizer server is configured to enable the outbiddingpurchasable-unit distributor to receive or access the user-centricinformation associated with the user to generate the offer.
 17. Thepoint-of-recognition optimizer method of claim 15, wherein theuser-centric information comprises at least portion of the user-centricdata used to train the optimizer machine learning model.
 18. Thepoint-of-recognition optimizer method of claim 14, wherein the one ormore of purchasable-units include the recognized purchasable-unit andone or more competing purchasable-units.
 19. The point-of-recognitionoptimizer method of claim 14, wherein the optimizer device displays anupdated cost savings value based on the offer from the outbiddingpurchasable-unit distributor.
 20. A point-of-recognition optimizermethod for optimizing user purchases of offsite purchasable-units, themethod comprising: receiving at an optimizer server, via a computertransmission from an optimizer device associated with a user, apurchasable-unit identifier (ID) associated with a purchasable-unit, thepurchasable-unit displayed on the optimizer device, and thepurchasable-unit available for purchase via the optimizer device;generating, by an optimizer machine learning model trained withuser-centric data, a user action score defining a probability of theuser to engage in a purchase of one or more of purchasable-unitsassociated with the purchasable-unit ID, the optimizer machine learningmodel communicatively coupled to the optimizer server; transmitting,from the optimizer server, the user action score to one or moredistributor servers associated with one or more competingpurchasable-unit distributors, the optimizer server configured toreceive, from the one or more distributor servers, one or more offerscorresponding to the one or more of purchasable-units, the one or morepurchasable-units located offsite from the user's current location; andtransmitting from the optimizer server, via a second computertransmission to the optimizer device, an offer of an offeredpurchasable-unit, the offer originating from the one or more distributorservers of an outbidding purchasable-unit distributor, the offer chosenfrom the one or more offers, wherein the one or more distributor serversof the outbidding purchasable-unit distributor outbid the one or moredistributor servers of all other distributors of the competingpurchasable-unit distributors for an opportunity of the optimizer deviceto receive the offer.